genetic algorithms

Using evolutionary algorithms to explore single-cell heterogeneity and microcircuit operation in the hippocampus

The hippocampus-entorhinal system is critical for learning and memory. Recent cutting-edge single-cell technologies from RNAseq to electrophysiology are disclosing a so far unrecognized heterogeneity within the major cell types (1). Surprisingly, massive high-throughput recordings of these very same cells identify low dimensional microcircuit dynamics (2,3). Reconciling both views is critical to understand how the brain operates. " "The CA1 region is considered high in the hierarchy of the entorhinal-hippocampal system. Traditionally viewed as a single layered structure, recent evidence has disclosed an exquisite laminar organization across deep and superficial pyramidal sublayers at the transcriptional, morphological and functional levels (1,4,5). Such a low-dimensional segregation may be driven by a combination of intrinsic, biophysical and microcircuit factors but mechanisms are unknown." "Here, we exploit evolutionary algorithms to address the effect of single-cell heterogeneity on CA1 pyramidal cell activity (6). First, we developed a biophysically realistic model of CA1 pyramidal cells using the Hodgkin-Huxley multi-compartment formalism in the Neuron+Python platform and the morphological database Neuromorpho.org. We adopted genetic algorithms (GA) to identify passive, active and synaptic conductances resulting in realistic electrophysiological behavior. We then used the generated models to explore the functional effect of intrinsic, synaptic and morphological heterogeneity during oscillatory activities. By combining results from all simulations in a logistic regression model we evaluated the effect of up/down-regulation of different factors. We found that muyltidimensional excitatory and inhibitory inputs interact with morphological and intrinsic factors to determine a low dimensional subset of output features (e.g. phase-locking preference) that matches non-fitted experimental data.